System, method, and computer algorithm for characterization and classification of electrophysiological evoked potentials

ABSTRACT

An automated EP analysis apparatus for monitoring, detecting and identifying changes (adverse or recovering) to a physiological system generating the analyzed EPs, wherein the apparatus is adapted to characterize and classify EPs and create alerts of changes (adverse or recovering) to the physiological systems generating the EPs if the acquired EP waveforms change significantly in latency, amplitude or morphology.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 61/641,583 filed May 2, 2012 entitled “SYSTEM, METHOD, ANDCOMPUTER ALGORITHM FOR CHARACTERIZATION AND CLASSIFICATION OFELECTROPHYSIOLOGICAL POTENTIALS” which is hereby incorporated herein byreference in its entirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates generally to detecting changes in evokedpotentials (EPs), and more particularly to detecting changes in EPsautomatically with a computer algorithm.

2. Related Art

Somatosensory evoked potentials are summated electrical potentialsusually recorded from the head or neck area after repeatedly stimulatinga peripheral nerve. Monitoring patients using somatosensory evokedpotentials during surgery has been shown to allow early identificationof impending positioning effect injury that can then be avoided byrepositioning the patient to alleviate pressure or tension.

For example, as described in Hickey, C.; Gugino, L. D.; Aglio, L. S.;Mark, J. B.; Son, S. L. & Maddi, R. (1993), “Intraoperativesomatosensory evoked potential monitoring predicts peripheral nerveinjury during cardiac surgery,” Anesthesiology 78(1), 29-35, Kamel, I.R.; Drum, E. T.; Koch, S. A.; Whitten, J. A.; Gaughan, J. P.; Barnette,R. E. & Wendling, W. W. (2006), “The use of somatosensory evokedpotentials to determine the relationship between patient positioning andimpending upper extremity nerve injury during spine surgery: aretrospective analysis,” Anesth Analg 102(5), 1538-1542, and Labrom, R.D.; Hoskins, M.; Reilly, C. W.; Tredwell, S. J. & Wong, P. K. H. (2005),and “Clinical usefulness of somatosensory evoked potentials fordetection of brachial plexopathy secondary to malpositioning inscoliosis surgery.” Spine 30(18), 2089-2093, the above incorporated byreference in their entirety's.

Such monitoring generally requires highly trained technologists underphysician supervision with sophisticated, multichannel amplifier anddisplay equipment. Unfortunately, such personnel and equipment arelimited in their availability, require pre-booking, and are costly. Inaddition, such monitoring is not traditionally done in many of the areasin which positioning effects occur outside of the operating room whereunresponsive, weak or confined patients may incur positioning effect.

To acquire and record the EPs, a technologist connects electrodes placedon the patient to a neuromonitoring instrument that evokes, acquires,processes and displays the waveforms. Typically, the technologistreviews the waveforms while a neurologist contemporaneously reviews theEP waveforms either on site or remotely through the internet. Thetechnologist and neurologist are trained and are experts in determiningwhether the changes in the EP waveforms are significant and areindicative of pending nerve injury. The cost of having professionalsfully engaged in interpreting these waveforms results in rationing ofthe service to all but the most high risk surgeries.

U.S. Patent Application Publication No. 2008/0167574 describes asemiautomated device available for automatically measuring biometricsignals during surgery to avoid nerve injury. However, the devicefocuses on muscle or motor recordings to measure nerve proximity tosurgical instruments and does not address positioning effect.

The difficulty with analyzing and classifying waveforms to identifypositioning effect lies in the wide variation in the amplitude,frequency and shape of the waveforms. These variations are caused bymany factors including anesthesia, electrical interference from otherdevices and any preexisting abnormalities of the nerves.

Accordingly, there is a need for a system and method that can overcomethe disadvantages of previous systems and methods.

SUMMARY OF THE INVENTION

In an exemplary embodiment of the present invention, a system, method,and computer algorithm for characterization and classification ofelectrophysiological EPs is disclosed. An EP may be defined as a voltageversus time signal obtained by a neural system using suitableelectrodes. For example when obtaining an EP from a somatosensory systema signal may be obtained by ensemble averaging the electrophysiologicalresponses to repetitive stimulation of the somatosensory system detectedusing suitable electrodes. Examples of EPs are somatosensory, auditoryor visual EPs. The algorithms are applied to a time sequence of EPsacquired over the course of an ongoing clinical procedure. Thealgorithms establish the characteristics of a baseline/normal EP andthen characterize subsequent EPs relative to the baseline EP as well asto previous EPs to determine if the functioning of the underlyingsensory neural system has been significantly affected by the ongoingclinical procedure. The algorithms communicate with ancillary hardwareand algorithms developed to acquire the sequence of EPs and providesuitable feedback to ensure an effective clinical workflow. Thealgorithms provide the basis for a clinically effective application suchthat false positives and false negatives are minimized.

Further features and advantages of the invention, as well as thestructure and operation of various embodiments of the invention, aredescribed in detail below with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the invention will beapparent from the following, more particular description of a preferredembodiment of the invention, as illustrated in the accompanying drawingswherein like reference numbers generally indicate identical,functionally similar, and/or structurally similar elements.

FIG. 1 illustrates an exemplary depiction of stimulation of aphysiological system of interest with a context relevant stimulusaccording to an exemplary embodiment of the present invention.

FIG. 2 illustrates an exemplary depiction of a sequence of suitablestimuli applied to a physiological system of interest and the sequenceof corresponding responses according to an exemplary embodiment of thepresent invention.

FIG. 3 illustrates an exemplary depiction of the creation of an ensembleaveraged EP based on a number of responses according to an exemplaryembodiment of the present invention.

FIG. 4A illustrates an exemplary flowchart process for acquiring andclassifying EP responses according to an exemplary embodiment of thepresent invention.

FIG. 4B illustrates an exemplary flowchart process for determiningwhether a change has occurred in a sequence of EPs according to anexemplary embodiment of the present invention.

FIG. 5 illustrates an exemplary flowchart process for calculating abaseline response according to an exemplary embodiment of the presentinvention.

FIG. 6 illustrates an exemplary flowchart process for determining theanalysis range according to an exemplary embodiment of the presentinvention.

FIG. 7 illustrates an exemplary flowchart process for updating abaseline response according to an exemplary embodiment of the presentinvention.

FIG. 8 illustrates an exemplary embodiment of a relationship diagram inmetric calculation for characterizing EPs according to an exemplaryembodiment of the present invention.

FIG. 9 illustrates an exemplary flowchart process for a good stateaccording to an exemplary embodiment of the present invention.

FIG. 10 illustrates an exemplary flowchart process for a bad stateaccording to an exemplary embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Various exemplary embodiments of the invention including preferredembodiments are discussed in detail below. While specific exemplaryembodiments are discussed, it should be understood that this is done forillustration purposes only. A person skilled in the relevant art willrecognize that other components and configurations can be used withoutparting from the spirit and scope of the invention.

An embodiment of the present invention relates to the computer signalprocessing and pattern recognition algorithms for the characterizationand classification of EPs in real-time. This algorithm may substitutefor the expert analysis typically provided by the technologist andphysician. The computer algorithm running in software installed on an EPmachine may be used in any surgery or situation where a patient is atrisk to detect, alert and ameliorate positioning effect or anyabnormality.

FIG. 1 illustrates an exemplary stimulation of a physiological system ofinterest with a context relevant stimulus according to an exemplaryembodiment of the present invention. For the somatosensory system, forexample, a stimulus may be the application of an appropriate sized andshaped current pulse over a superficial nerve. Given that a suitablestimulus is applied, the electrophysiological response is then a volleyof action potentials along the axons excited by the applied stimulus.

FIG. 2 illustrates an exemplary depiction of a sequence of suitablestimuli applied to a physiological system of interest and the sequenceof corresponding responses. The sequence of corresponding responses maybe detected using suitable electrodes suitably configured at a suitablerecording site according to an exemplary embodiment of the presentinvention. These responses are comprised of time sampled and digitizedmeasurements of the volume conducted voltage fields created by theelectrophysiological response of the physiological system of interestwhen evoked by the applied stimuli.

FIG. 3 illustrates an exemplary depiction of the creation of an ensembleaveraged EP based on a number of responses according to an exemplaryembodiment of the present invention. Individual responses may becontaminated by voltage contributions from the electrophysiologicalactivity of other physiological systems as well as ambient electricalnoise. As such, in order to obtain a suitable signal to noise ratio, anumber of responses may be ensemble averaged to create a resultingevoked potential (EP). The signal to noise ratio of the resulting EPimproves as N, the number of responses averaged, increases. In anembodiment, N may range from 10 to 1000 depending on the physiologicalsystem of interest.

EPs may be processed to assess the state of the physiological system ofinterest. A physiological system in a normal operating mode may beconsidered to be in a “Good” state. If the physiological system isstressed, fatigued, or injured, the system may be considered to be in a“Bad” state. Starting with the physiological system in a Good state,detected changes in the characteristics of the EPs in a sequence of EPscan be used to predict if the physiological system is in a Good or Badstate.

FIG. 4A illustrates an exemplary flowchart process for acquiring andclassifying EP responses according to an exemplary embodiment of thepresent invention. Each EP may be initially filtered to remove unwantedinstrumentation noise to better present the electrophysiologicalresponse of the system of interest. The EPs may be filtered based onlikelihood estimation.

If a baseline response does not exist, acquired responses may beanalyzed to estimate a baseline response and to establish an analysisrange. For example, if there is not N_(I) Good responses received, whereN_(I) is a number of initial EP responses required to create a baselineresponse, then a baseline response may not exist. The analysis toestimate a baseline response and to establish an analysis range isfurther described below.

If a baseline response exists, the baseline may be updated based on thecurrent response. Updating the baseline is further described below.

Once the current baseline response is determined, the current responseis then characterized relative to the current baseline and previousresponse. For example, characterization may at least one of a Euclideandistance, a pseudo-correlation, a cross-correlation, or an energy ratiobetween the current response and current baseline. Energy ratio may bethe ratio of the energy between the current response and the currentbaseline. The energy ratio may represent a change in size of the EPresponse. The current response may be then be classified based on thecurrent response's characterization.

EPs may be classified into four possible categories: Good, Bad,Undetermined and Unreliable based on the characterization. A Goodclassification may indicate the EP characterization corresponds with nosignificant waveform change. For example, when there is no positioningeffect. A Bad classification may indicate the EP characterizationcorresponds with a signification waveform change. For example, whenthere is positioning effect. An Undetermined classification may indicatethat the EP characterization may be of indeterminate significance. Forexample, the EP characterization may be insufficient for a Goodclassification but also insufficient for a Bad classification. Forexample, the EP may possibly correspond with either positioning effector no positioning effect. An unreliable classification may indicate theEP includes too much noise to be properly characterized and classified.

Each classification may correspond with a particular threshold. Thethreshold may indicate how similar an EP response should be to abaseline to be considered a Good response or how dissimilar an EPresponse should be to a baseline to be considered a Bad response. Thethresholds may be based on the characterizations of the EP responses.For example, thresholds may be based on at least one of Euclideandistance, a pseudo-correlation, a cross-correlation, or an energy ratiobetween an EP response and a baseline. A threshold may also indicate howmuch noise may be included in an EP response before the EP response isconsidered unreliable.

The thresholds used for classification may be determined by analyzingtraining data. Training data may include a plurality of EP responsesknown to correspond to particular classifications. Using multiple setsof thresholds determined from the analysis of training data, the currentresponse may be classified as belonging to a category of interest basedon the values of its calculated metrics.

FIG. 4B illustrates an exemplary flowchart process for determiningwhether a change has occurred in a sequence of EPs according to anexemplary embodiment of the present invention. FIG. 4B continues fromFIG. 4A. Given the sequence of classified EPs, it may be determinedwhether the state of the physiological system of interest has changed(either from Good to Bad or vice versa) or if the state of thephysiological system of interest has not changed. If the state haschanged, the system may create an alert.

FIG. 5 illustrates an exemplary flowchart process for calculating abaseline response according to an exemplary embodiment of the presentinvention. Currently loaded responses may be iteratively represented asnodes within a minimum spanning tree (MST) created using the Euclideandistances between response pairs. Each line in the MST that links pairsof responses may represent a Euclidean distance value. The currentlyloaded responses may be initially acquired responses. Response pairs maybe combinations of any two currently loaded responses. For example,three responses may result in three response pairs. The Euclideandistance may be based on the sum of the squares of the differencesbetween responses in each response pair or the sum of the absolute valueof the differences between responses in each response pair.

The MST may be separated into clusters based on cutting lines that aregreater than a threshold. The threshold may be based on the mean of theline lengths and standard deviations of the line lengths. The clustersmay be sorted based on the sizes of the clusters. The size of a clustermay be the number of responses within the cluster. The cluster with thelargest size may be selected so that a temporary baseline is calculatedbased on the responses within the cluster. All the responses within thelargest cluster may be aligned using a default analysis range andpseudo-correlation. The response members of the cluster with the largestnumber of members may be averaged to estimate the baseline response.

FIG. 6 illustrates an exemplary flowchart process for determining theanalysis range according to an exemplary embodiment of the presentinvention. Initial responses are characterized and classified usinginitial baseline response estimates and a default analysis range. First,initial Good responses are used to locate a default width analysis rangeby adjusting the location of the range until a minimum congruity valueis obtained. Using the initial Good responses, the width of the analysisrange is then adjusted by increasing it to the left or right until aminimum congruity value is obtained. For both analysis range locationand sizing, the congruity measure may be:

$\frac{1}{3}\left\lbrack {{2*{NormED}} + \frac{1}{CC}} \right\rbrack$

-   -   where NormED is a normalized Euclidean distance and CC is the        cross-correlation. While not shown in FIG. 6 , the calculated        new baseline response may be used to re-calculate the analysis        range.

FIG. 7 illustrates an exemplary flowchart process for updating abaseline response according to an exemplary embodiment of the presentinvention. As shown in FIG. 7 , if a previous response is classified asgood, the current baseline may be recalculated based on the previousresponse and the previous baseline. For example, the current baselinemay be set to 25% of the previous response and 75% of the previousbaseline. If the previous response is not classified as good, thecurrent baseline may be set to the previous baseline.

Regardless of how the new current baseline is determined, the newcurrent baseline may be used to re-align the current response relativeto the new current baseline. Metric calculation may then be performed onthe re-aligned response.

FIG. 8 illustrates an exemplary embodiment of a relationship diagram inmetric calculation for characterizing EPs according to an exemplaryembodiment of the present invention. As shown in FIG. 8 , a currentresponse may be compared with a previous response to give a Euclideandistance between the responses, a pseudo-correlation, and across-correlation. A current response may be compared with a currentbaseline to give a Euclidean distance between the response and baseline,a pseudo-correlation, a cross-correlation, and an energy ratio. Thecurrent response may be classified based on these various results.

After a next response is acquired, the current response may also be usedto give a Euclidean distance between the current response and nextresponse, a pseudo-correlation, and a cross-correlation.

FIG. 9 illustrates an exemplary flowchart process for a good stateaccording to an exemplary embodiment of the present invention. If a Badresponse is received while in the Good state, the system may check tosee if a bad counter is greater than or equal to a bad counterthreshold, N_(B). The bad counter may indicate a number of Badresponses. The bad counter threshold N_(B) may indicate the number ofBad responses or undetermined responses to receive before the next Badresponse changes the state to a bad state. The bad counter thresholdN_(B) may be set for each state depending on the physiological system ofinterest.

If the bad counter is greater than the bad counter threshold N_(B), thenthe current state may be changed to the Bad state and an alert may becreated. The alert may be conveyed to a user of the system in a varietyof ways, e.g., with displaying visualizations, generating sounds,creating vibrations, etc. If the bad counter is not greater than badcounter threshold N_(B), then the bad counter may be incremented and theBad response added to a bad tracker. The bad tracker may track the Badresponses and Undetermined responses received.

If the response received is not a bad response, the system may check ifthe response received is an undetermined response. If the responsereceived is an undetermined response, then the bad counter is alsoincremented and the undetermined response is added to the bad tracker.

If the response received is also not an undetermined response, thesystem may check if the response received is a good response. If theresponse received is a good response, then if the bad counter is lessthan or equal to the bad counter threshold N_(B), then the bad counteris reset to zero and the bad tracker is emptied. If the bad counter isgreater than bad counter threshold N_(B), then the good counter may beincremented and the Good response added to the Good tracker.

If the response received is also not a good response, then the systemmay determine that the response is an unreliable response and may ignorethe response.

Based on the bad counter, the bad tracker, the good counter, and thegood tracker, the system may provide different indications to a user.The system may change the color of an icon displayed so that the iconappears green when the bad counter is zero and gradually becomes redderwith increasing values for the bad tracker.

FIG. 10 illustrates an exemplary flowchart process for a bad stateaccording to an exemplary embodiment of the present invention. If a goodresponse is received while in the bad state, the system may increment agood counter, and, if the bad counter is less than the bad counterthreshold N_(B), clear the bad tracker check.

The system may check to see if a good counter is greater than or equalto a good counter threshold, N_(G). The good counter may indicate anumber of good responses. The good counter threshold N_(G) may indicatethe number of good responses needed to be received to change the stateto a good state. The good counter threshold N_(G) may be set for eachstate depending on the physiological system of interest. If the goodcounter is greater than the good counter threshold N_(G), then thecurrent state may be changed to the good state and an alert may becreated. If the good counter is not greater than good counter thresholdN_(G), then the good response may be added to a good tracker. The goodtracker may track the good responses received.

If the response received is not a good response, the system may check ifthe response received is an undetermined response. If the responsereceived is an undetermined response, then the bad counter isincremented and the undetermined response is added to the bad tracker.

If the response received is also not an undetermined response, thesystem may check if the response received is a bad response. If theresponse received is a bad response, then if the good counter is lessthan or equal to the good counter threshold N_(G), then the good counteris reset to zero and the good tracker is emptied. If the good counter isgreater than good counter threshold N_(G), then the bad counter may beincremented and the bad response added to the bad tracker.

If the response received is also not a bad response, then the system maydetermine that the response is an unreliable response and may ignore theresponse.

The signal processing routines may be applied to reduce the noise in theacquired EPs and to detect when EPs with inadequate signal to noiseratio (SNR) are acquired so that these EPs may be excluded from furtheranalysis and the poor signal quality reported. The number of unreliablesignals received may be tracked and compared with a threshold todetermine when to create an alert regarding poor signal quality.

The filtering techniques applied may use likelihood-estimation basedaveraging to decrease instrumentation and context-based noise andincrease the SNR of the acquired EPs such that baseline EPs can be moreclearly defined and that changes in subsequent EPs can be bettercharacterized and compared to the baseline and previous EPs.

Pattern recognition algorithms may be used to characterize the EPs, tomeasure changes in latter acquired EPs relative to the baseline andprevious EPs and to detect when changes to the EPs, indicative of achanged functioning of the underlying sensory neural system, haveoccurred. EPs may be characterized using their energy, Euclideandistance and pseudo and cross correlations relative to a definedbaseline template response as well as to previous EPs. Using thesemetrics, classification rules may be applied to determine if the currentresponse indicates significant (adverse or recovering) changes to theunderlying physiological system generating the EPs.

In an embodiment, a component may be added to allow medical or otherattending personnel to reset the baseline response when the changes inthe acquired EPs are not related to any underlying physiological change(e.g., changes related to stimulation or electrode factors).

In an embodiment, the system may be an automated EP analysis apparatusfor monitoring, detecting and identifying changes (adverse orrecovering) to a physiological system generating the analyzed EPs,wherein the apparatus is adapted to characterize and classify EPs andcreate alerts of changes (adverse or recovering) to the physiologicalsystems generating the EPs if the acquired EP waveforms changesignificantly in latency, amplitude or morphology. The system mayfurther include a system to integrate such apparatus into other devicesin a surgical environment.

The apparatus may also feed information to other devices in the surgicalenvironment that allows these devices to manually or automaticallyameliorate or mitigate the physiological changes and improvesubsequently acquired EP waveforms.

The apparatus may also obtain information from an anesthesia or bloodpressure machine to calculate when changes in EP waveforms are due toanesthesia or blood pressure changes.

The apparatus may perform a method of automatically identifyingpotential injury to peripheral nerve structures including stimulatingperipheral nerves with electrical pulses, recording resultant electricalwaveforms generated by the nervous system through electrodes placed atthe neck or head, measuring changes or trends in the acquired EPwaveforms, alerting the user to the changes, allowing the user theoption to decide if the data is accurate, passing that information to anautomated operating room table, and automatically or semi automaticallyreadjusting patient position through adjustment of the table toameliorate or avoid injury.

While various embodiments of the present invention have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. Thus, the breadth and scope of thepresent invention should not be limited by any of the above-describedexemplary embodiments, but should instead be defined only in accordancewith the following claims and their equivalents.

1-5. (canceled)
 6. An automated evoked potential (EP) analysis apparatusfor automatic monitoring, detecting, and identifying of changes (adverseor recovering) to a physiological system generating EP waveforms,wherein the apparatus is adapted to: analyze the signal-to-noise ratioof acquired EP waveforms; update the baseline using EP waveforms thatare analyzed to have an adequate signal-to-noise ratio; exclude each EPwaveform having an inadequate signal-to-noise ratio; track the number ofinadequate EP waveforms; and generate an alert if the number ofinadequate EP waveforms exceeds a threshold.
 7. The apparatus of claim6, wherein the analyzing the signal-to-noise ratio of acquired EPwaveforms is achieved using likelihood estimation-based averaging. 8.The apparatus of claim 6, wherein the apparatus is further adapted toautomatically acquire and establish the baseline using real-time EPwaveforms.
 9. The apparatus of claim 6, wherein the apparatus is furtheradapted to characterize and classify the EP waveforms and create alertsof the changes (adverse or recovering) to the physiological systemgenerating the EP waveforms if the acquired EP waveforms changesignificantly in latency, amplitude or morphology relative to abaseline.
 10. The apparatus of claim 6, further comprising a system tointegrate the apparatus into other devices in a surgical environment.11. The apparatus of claim 6, wherein said apparatus is configured tofeed information to other devices in a surgical environment to therebyallow the other devices to manually or automatically ameliorate ormitigate the changes and improve subsequently acquired EP waveforms. 12.The apparatus in claim 6, wherein said apparatus is configured to obtaininformation from an anesthesia or blood pressure machine to calculatewhen changes in the EP waveforms are due to anesthesia or blood pressurechanges.
 13. The apparatus of claim 6, wherein the apparatus comprisesmemory and a processor, wherein the processor is configured to executeinstructions stored on the memory, that, when executed by the processor,cause the processor to perform operations comprising: stimulating, viaelectrical pulse electrodes, a peripheral nerve structure; recording,via electrodes placed at the neck or head, signals forming a resultantelectrical (EP) waveform generated by the nervous system in response tothe stimulation; measuring a waveform change in the recorded EP waveformrelative to a baseline response, the waveform change comprising aEuclidean distance, a pseudo-correlation, a cross-correlation, and anenergy ratio between the recorded EP waveform and the baseline response;comparing the waveform change with one or more threshold change valuesto classify the recorded EP waveform; and determining whether a currentstate of the peripheral nerve structure has changed based on theclassification of the recorded EP waveform by determining whether acount of EP waveforms in a classification exceeds a threshold countvalue.
 14. The apparatus of claim 13, wherein the operations furthercomprise: alerting a user to the waveform changes; and providing theuser with an option to decide if data associated with the waveformchanges is accurate.
 15. The apparatus of claim 13, wherein theoperations further comprise: transmitting the current state to anautomated operating room table; and automatically or semi automaticallyreadjusting a patient position through adjustment of the table toameliorate or avoid injury.
 16. The apparatus of claim 13, wherein thewaveform change comprises a Euclidian distance and at least one from agroup consisting of: a pseudo-correlation, a cross-correlation, and anenergy ratio between the recorded EP waveform and the baseline response.17. The apparatus of claim 13, wherein the operations further compriseclassifying the recorded EP waveform into four possible categories:good, bad, undetermined, and unreliable based on the change relative tothe baseline response.
 18. The apparatus of claim 17, wherein thethreshold change value for a good classification relates to how similarthe recorded EP waveform should be to the baseline response.
 19. Theapparatus of claim 13, wherein a threshold change value relates to howsimilar the recorded EP waveform should be to the baseline response. 20.The apparatus of claim 13, wherein determining whether the count of EPwaveforms in the classification exceeds the threshold count valuecomprises: counting a number of EP responses that are classified in agiven classification; comparing the current count to the threshold countvalue; and if the current count exceeds the threshold count value,changing the state of the peripheral nerve structure to the state thatcorresponds with the exceeded threshold count value.
 21. The apparatusof claim 13, wherein the operations further comprise generating an alertif the current state of the peripheral nerve structure has changed.